Mitigating Outlier Effect in Online Regression: An Efficient Usage of Error Correntropy Criterion
Mitigating Outlier Effect in Online Regression: An Efficient Usage of Error Correntropy Criterion
复制标题
DOI：
10.1109/ijcnn48605.2020.9207141
复制
发表时间：
2020-07
期刊：
影响因子：
--
通讯作者：
Sajjad Bahrami;E. Tuncel
中科院分区：
文献类型：
--
作者：
Sajjad Bahrami;E. Tuncel
本文提出了一种改进的最大相关熵准则（MCC），并将其应用于在线回归（或自适应滤波）。众所周知，当系统输出和标签（有时称为期望信号）之间的误差包含离群值和/或不遵循高斯分布时，诸如误差相关熵准则（ECC）和误差熵准则（EEC）的信息理论准则在监督学习问题（如回归和自适应滤波）中具有更好的性能的优点。具体来说，我们改进了现有的自适应最大相关熵准则算法（称为AMCC），简单地消除主要离群值在学习过程中。这种消除导致比以前已知的算法更好的稳态性能。
In this paper, a modified version of maximum correntropy criterion (MCC) with application in online regression (or adaptive filtering) is proposed. It is well known that information theoretic criteria such as error correntropy criterion (ECC) and error entropy criterion (EEC) have the advantage of better performance in supervised learning problems like regression and adaptive filtering when the error between system output and labels (sometimes called desired signals) contains outliers and/or does not follow a Gaussian distribution. Specifically, we improve the existing adaptive maximum correntropy criterion algorithm (known as AMCC) by simply eliminating major outliers during learning process. This elimination leads to better steady state performance than previously known algorithms.